Taking a row from a pandas DataFrame and adding it to a new DataFrame

Question

So, I have a DataFrame with with 14 columns (with headers). I’d like to take the values of a row of this DataFrame at an index that’s a random integer between 0 and the maximum length of the DataFrame, extract those values, and then add them to a new DataFrame with the same headers.

The data frame looks something like this:

I’ve tried using various combinations with .iloc, but for some reason that produces a DataFrame that’s nothing but column headers, not the actual numerical values of the DataFrame itself.

What’s the best way to do this?

Thanks

Asked By: Tessa

||

Source

Answer 1

Assuming that your dataframe is named df, you should be able to query a specific row using iloc, e.g.:

df.iloc[[i]]

Where i is your random number.

You can then concatenate this dataframe with your new dataframe df_new.

for i in np.random.randint(0,10,size=10):
    if df_new.empty:
        df_new = df.iloc[[i]]
    else:
        df_new = pd.concat([df_new, df.iloc[[i]]])

Answered By: Sheldon

Answer 2

Let’s say you have same dummy dataframe like this:

df = pd.DataFrame({'A': ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'],
                   'B': ['one', 'one', 'two', 'two', 'one', 'one'],
                   'C': [1, 2, 3, 4, 5, 6],
                   'D': [7, 8, 9, 10, 11, 12]})

Let’s create an emplty df:

df2 = pd.DataFrame(columns=df.columns)

If you want take a row from df to df2 then just do this:

df2 = df2.append(df.iloc[1], ignore_index=True)
# if append is deprecated you can do this:
df2 = pd.concat([df2, df.iloc[[1]]], axis=0, ignore_index=True)

If you want a random one you can do:

df2 = df2.append(df.sample(1), ignore_index=True)
#or
df2 = pd.concat([df2, df.sample(1)], ignore_index=True)

Answered By: TheEngineerProgrammer

Taking a row from a pandas DataFrame and adding it to a new DataFrame

Question:

Answers: